Which test for cytokine production? - by PBMCs stimulated with proteins (Aug/23/2009 )

I would like to get your opinion which test should I use for statistical analysis of my experimental data.

The experiment involved stimulation of human PBMCs with certain proteins followed by measurement of cytokines production response (in pg/mL). I have blank sample (only medium, non-stimulated) and proteins #1 to #5, each in 3 to 12 replicates. I would like to compare "everything with everything" (for each cytokine separately of course).

My knowledge of statistics is limited and usually for that purpose I use one-way ANOVA with Tukey post-hoc test (also because software I use gives me clear table "everything vs everything" with P values).

-K.B.-

Your sample size is too small for a parametric test like ANOVA, unless you can demonstrate that your sample data is normally distributed. You need the non-parametric equivalent which is the Kruskal-Wallis test, followed by a post-hoc such as Tukey's

-bob1-

bob1 on Aug 26 2009, 07:45 PM said:

Your sample size is too small for a parametric test like ANOVA, unless you can demonstrate that your sample data is normally distributed. You need the non-parametric equivalent which is the Kruskal-Wallis test, followed by a post-hoc such as Tukey's

I've been told by a biostatistician that works for our department that biological data (experimental) is sometimes hard to get normally distributed, at least at the sample sizes commonly used, but that it can be assumed in most cases. Homogeneity of variance is one he recommends always be followed (and tested), though.

The one thing to be careful of is the 3 to 12 replicates noted. There are different things to look at when population sizes vary (I don't remember exactly what they are, it's been awhile since stats classes), and running the stats the same as you would if every sample had the same n would give you an incorrect significance.

For the "everything to everything", as bob1 said, a post hoc pairwise is required. I like Tukey's. I think it's a little less conservative than some other tests, like Bonferroni's or Scheffe's. Dunnett's is a good test to use if you only want to compare each treated sample to the untreated sample.

Remember, the more comparisons you do, the more conservative your tests become.

-fishdoc-

bob1 on Aug 27 2009, 02:45 AM said:

Your sample size is too small for a parametric test like ANOVA

What is the minimal sample size for ANOVA?

bob1 on Aug 27 2009, 02:45 AM said:

You need the non-parametric equivalent which is the Kruskal-Wallis test, followed by a post-hoc such as Tukey's

Thank you very much, I'll do that.

-K.B.-

fishdoc on Aug 27 2009, 03:43 AM said:

Homogeneity of variance is one he recommends always be followed (and tested), though.

How do I do that and what's does that mean to me?

As for 3-12 replicates - I have more replicates (up to 12) for "blanks" than I have for samples (no less then 3), because experiment was made on several plates.

-K.B.-

K.B. on Aug 31 2009, 10:44 AM said:

fishdoc on Aug 27 2009, 03:43 AM said:

Homogeneity of variance is one he recommends always be followed (and tested), though.

How do I do that and what's does that mean to me?

As for 3-12 replicates - I have more replicates (up to 12) for "blanks" than I have for samples (no less then 3), because experiment was made on several plates.

A simple way to test for it is to plot your mean against your standard deviation or standard error or something like that. If your deviation from the mean increases with you mean, then you may not have homogeneity of variance. On your plot, if you apply a linear equation to it and it's a pretty steep slope, that will tell you.

Levene's test or Brown-Forsythe's test will tell you if there is significant or insignificant heterogeneity of variance. You want a P value of > 0.05 for that test, meaning insignificant heterogeneity of variance or the variance is homogenous.

ANOVA assumes homogeneity of variance, so it's pretty important, and in my estimation, many times overlooked.

As for what sample size is enough, I think 2 is enough, but the lower you get, the less power you have, and the higher chance of getting insignificant P values when the treatments truly result in significant differences.

-fishdoc-

bob1 on Aug 27 2009, 02:45 AM said:

You need the non-parametric equivalent which is the Kruskal-Wallis test, followed by a post-hoc such as Tukey's

I just checked my software and I can make Kruskal-Wallis - but without post-hoc...